#!/usr/bin/python
# -*- coding:utf-8 -*-
# @FileName : DL7_test3_1.py
# Author    : myh

import torch
from torch import nn
from d2l import torch as d2l


def nin_block(in_channels, out_channels, kernel_size, strides, padding):
    return nn.Sequential(
        nn.Conv2d(in_channels, out_channels, kernel_size, strides, padding),
        nn.ReLU(),
        nn.Conv2d(out_channels, out_channels, kernel_size=1), nn.ReLU(),
        nn.Conv2d(out_channels, out_channels, kernel_size=1), nn.ReLU())

if __name__ == '__main__':
    net = nn.Sequential(
        nin_block(1, 96, kernel_size=11, strides=4, padding=0),
        nn.MaxPool2d(3, stride=2),
        nin_block(96, 256, kernel_size=5, strides=1, padding=2),
        nn.MaxPool2d(3, stride=2),
        nin_block(256, 384, kernel_size=3, strides=1, padding=1),
        nn.MaxPool2d(3, stride=2),
        nn.Dropout(0.5),
        # 标签类别数是10
        nin_block(384, 10, kernel_size=3, strides=1, padding=1),
        nn.AdaptiveAvgPool2d((1, 1)),
        # 将四维的输出转成二维的输出，其形状为(批量大小,10)
        nn.Flatten())


    lr, num_epochs, batch_size = 0.1, 10, 128
    train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
    d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
    d2l.plt.show()